****Figure 1***

**Figure 1 was created in Excel. The following code, however produces the ambition of respondents for each of the four public service positions by sample
//Federal Employees Sample
use AGESReplicationData, clear
tab Elecamb
tab Judamb
tab Execamb
tab Statamb

//Law Student Sample
use LSReplicationData, clear
tab electedofficeamb
tab judicialamb
tab federalagencyamb
tab statelocalagencyamb

//General Population Sample
use NSReplicationData.dta, clear
tab elecamb
tab judamb
tab fedamb
tab statamb


****Figure A.2 ***

**Figure A.2 was created in Excel. The following code, however, produces the attraction of each component of public service for both samples
//Figure A.2 may have slightly different numbers due to rounding.

//General Population Sample
use NSReplicationData.dta, clear
*talking about solutions
tab Q34_7_1
*Meeting new people
tab Q34_7_2
* Fundraising
tab Q34_7_3
*The publicity
tab Q34_7_4
* voter persuasion
tab Q34_7_5
*The competition
tab Q34_7_6
*Lack of privacy
tab Q34_8_1
*Making Important Decisions
tab Q34_8_2
*Public scrutiny
tab Q34_8_3
*The schedule/travelling
tab Q34_8_4
* difficult on family
tab Q34_8_5
*the conflict
tab Q34_8_7

//Law Student Sample
use LSReplicationData, clear
*talking about solutions
tab reasons_1
*Meeting new people
tab reasons_2
*Fundraising
tab reasons_3
*The publicity
tab reasons_4
*voter persuasion
tab reasons_5
*The competition
tab reasons_6
*Lack of privacy
tab reasons_8
*Making Important Decisions
tab reasons_9
*Public scrutiny
tab reasons_10
*The schedule/travelling
tab reasons_11
*difficult on family
tab reasons_12
*the conflict
tab reasons_14





***********************************************
*******Results from Bureaucratic Sample********
***********************************************

use AGESReplicationData, clear

****Figure 2 (and Table A.5)***

	*Results for Table A.5*
reg Elecamb female age ib9.raceI ib5.educ1 party1 ideo if raceI!=1, robust
	regsave female using "CoeffPlotAGES.dta", detail(all) addlabel(Office, 1) replace
reg Statamb female age ib9.raceI ib5.educ1 party1 ideo if raceI!=1, robust
	regsave female using "CoeffPlotAGES.dta", detail(all) addlabel(Office, 3) append
reg Judamb female age ib9.raceI ib5.educ1 party1 ideo if raceI!=1, robust
	regsave female using "CoeffPlotAGES.dta", detail(all) addlabel(Office, 2) append
reg Execamb female age ib9.raceI ib5.educ1 party1 ideo if raceI!=1, robust
	regsave female using "CoeffPlotAGES.dta", detail(all) addlabel(Office, 4) append

	
	*Creating Figure 2
preserve
	use CoeffPlotAGES.dta, clear
		gen CI=stderr*1.96
		graph set window fontface "Times New Roman"

	serrbar coef CI Office, yline (0) yscale(range(-.4(.1).2)) ylabel (-.4(.1).2, labs(medium) nogrid) xscale(range(.90(1)4)) xlabel (1 "Elected Office" 2 "Judicial" 3 "State Agency" 4 "Federal Agency",  angle(35) labsize (medium) nogrid) xtitle ("") ytitle ("Effect of Gender on Ambition", size (medium) ) 
*	graph save CoeffPlotAGES, replace	

restore

****Table A.3***

tab female
tab age
tab educ
tab race
tab party1


****Table A.6***

ologit Statamb female age ib9.raceI ib5.educ1 party1 ideo if raceI!=1, robust
ologit Elecamb female age ib9.raceI ib5.educ1 party1 ideo if raceI!=1, robust
ologit Judamb female age ib9.raceI ib5.educ1 party1 ideo if raceI!=1, robust
ologit Execamb female age ib9.raceI ib5.educ1 party1 ideo if raceI!=1, robust

***********************************************
*******Results from Law Student Sample*********
***********************************************

use LSReplicationData, clear


****Figure 3 (and Table A.9)***

	*Results for Table A.9*
reg electedofficeamb female ideo pid7 i.race knowledge private, cluster (lawschool)
	regsave female using "CoeffPlot.dta", detail(all) addlabel(Office, 1) replace
reg statelocalagencyamb female ideo pid7 i.race knowledge private, cluster (lawschool)
	regsave female using "CoeffPlot.dta", detail(all) addlabel(Office, 3) append
reg judicialamb female ideo pid7 i.race knowledge private, cluster (lawschool)
	regsave female using "CoeffPlot.dta", detail(all) addlabel(Office, 2) append
reg federalagencyamb female ideo pid7 i.race knowledge private, cluster (lawschool)	
	regsave female using "CoeffPlot.dta", detail(all) addlabel(Office, 4) append
	
	*Creating Figure 3*
preserve
	use CoeffPlot.dta, clear
		gen CI=stderr*1.96
		graph set window fontface "Times New Roman"


	serrbar coef CI Office, yline (0) yscale(range(-.4(.1).2)) ylabel (-.4(.1).2, labs(medium) nogrid) xscale(range(.90(1)4)) xlabel (1 "Elected Office" 2 "Judicial" 3 "State Agency" 4 "Federal Agency",  angle(35) labsize (medium) nogrid) xtitle ("") ytitle ("Effect of Gender on Ambition", size (medium) ) 
*	graph save CoeffPlotLawStudent, replace	

restore	

****Figure 6 (and Table A.12)***

	*Results for Table A.12*
reg personalcomp ideo i.race female pid7 private, cluster (lawschool)
	regsave female using "CoeffPlotAttractLS.dta", detail(all) addlabel(Aspect, 1) replace

reg electoralcomp ideo i.race female pid7 private, cluster (lawschool)
	regsave female using "CoeffPlotAttractLS.dta", detail(all) addlabel(Aspect, 2) append

reg jobcomp ideo i.race female pid7 private, cluster (lawschool)
	regsave female using "CoeffPlotAttractLS.dta", detail(all) addlabel(Aspect, 3) append

	*Creating Figure 6*
preserve
	use CoeffPlotAttractLS.dta, clear
		gen CI=stderr*1.96
		graph set window fontface "Times New Roman"


	serrbar coef CI Aspect, yline (0) yscale(range(-.4(.1).2)) ylabel (-.4(.1).2, labs(medium) nogrid) xscale(range(.90(1)3)) xlabel (1 "Personal Life" 2 "Electoral" 3 "Job Responsibility",  angle(35) labsize (medium) nogrid) xtitle ("") ytitle ("Effect of Gender on Public Office Attractiveness", size (medium) ) 
*	graph save CoeffPlotAttractLawStudent, replace	

restore	

****Table A.2***

tab female
tab race
tab latino
tab pid7
tab ideology

****Table A.10***

ologit statelocalagencyamb female ideo pid7 i.race knowledge private, cluster (lawschool)
ologit electedofficeamb female ideo pid7 i.race knowledge private, cluster (lawschool)
ologit judicialamb female ideo pid7 i.race knowledge  private, cluster (lawschool)
ologit federalagencyamb female ideo pid7 i.race knowledge  private, cluster (lawschool)



***********************************************
*********Results from National Sample**********
***********************************************

use NSReplicationData.dta, clear

****Figure 4 (and Table A.7) ***

	*Results for Table A.7*
reg statamb female ideo5 PartyID i.race age educ income polknow polint, robust
	regsave female using "CoeffPlotGenPop.dta", detail(all) addlabel(Office, 3) replace
reg elecamb female ideo5 PartyID i.race age educ income polknow polint, robust
	regsave female using "CoeffPlotGenPop.dta", detail(all) addlabel(Office, 1) append
reg judamb female i.gender ideo5 PartyID i.race age educ income polknow polint, robust
	regsave female using "CoeffPlotGenPop.dta", detail(all) addlabel(Office, 2) append
reg fedamb female ideo5 PartyID i.race age educ income polknow polint, robust
	regsave female using "CoeffPlotGenPop.dta", detail(all) addlabel(Office, 4) append

	*Creating Figure 4*
preserve
	use CoeffPlotGenPop.dta, clear
		gen CI=stderr*1.96
		graph set window fontface "Times New Roman"

	serrbar coef CI Office, yline (0) yscale(range(-.4(.1).2)) ylabel (-.4(.1).2, labs(medium) nogrid) xscale(range(.90(1)4)) xlabel (1 "Elected Office" 2 "Judicial" 3 "State Agency" 4 "Federal Agency",  angle(35) labsize (medium) nogrid) xtitle ("") ytitle ("Effect of Gender on Ambition", size (medium) ) 
	graph save CoeffPlotGenPop(1), replace	

restore	

****Table 4***
**Variable labels are found in the section describing Figure A.2. The following code sorts the variables based on their factor loads and puts them in the order indicated in Table 4

factor Q34_7_2 Q34_7_1 Q34_7_3 Q34_7_4 Q34_7_5 Q34_7_6 Q34_8_1 Q34_8_2 Q34_8_3 Q34_8_4 Q34_8_5 Q34_8_7, pcf
rotate, promax
sortl

****Figure 5 (and Table A.11)***

	*Results for Table A.11*
reg campaigning female ideo5 PartyID i.race age educ income polknow polint, robust
	regsave female using "CoeffPlotAttractGenPop.dta", detail(all) addlabel(Aspect, 1) replace
reg diffcampaign female ideo5 PartyID i.race age educ income polknow polint, robust
	regsave female using "CoeffPlotAttractGenPop.dta", detail(all) addlabel(Aspect, 2) append
reg theJob female ideo5 PartyID i.race age educ income polknow polint, robust
	regsave female using "CoeffPlotAttractGenPop.dta", detail(all) addlabel(Aspect, 3) append


	*Creating Figure 5*
preserve
	use CoeffPlotAttractGenPop.dta, clear
		gen CI=stderr*1.96
		graph set window fontface "Times New Roman"

	serrbar coef CI Aspect, yline (0) yscale(range(-.4(.1).2)) ylabel (-.4(.1).2, labs(medium) nogrid) xscale(range(.90(1)3)) xlabel (1 "Personal Life" 2 "Electoral" 3 "Job Responsibility",  angle(35) labsize (medium) nogrid) xtitle ("") ytitle ("Effect of Gender on Public Office Attractiveness", size (medium) ) 
	graph save CoeffPlotAttractGenPop, replace	

restore	


****Table A.1***

tab female
sum age
tab educ
tab race
tab PartyID


****Table A.8***

ologit elecamb female ideo5 PartyID i.race age educ income polknow polint, robust
ologit statamb female ideo5 PartyID i.race age educ income polknow polint, robust
ologit judamb female i.gender ideo5 PartyID i.race age educ income polknow polint, robust
ologit fedamb female ideo5 PartyID i.race age educ income polknow polint, robust

